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AI CAPABILITY

Your Quotes Take Days. Your Competitors Quote in Hours.

Quoting is the revenue bottleneck nobody talks about. Your estimators read specs, pull historical data, calculate materials, and assemble proposals by hand. Every slow quote is a deal your competitor closes first.

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01
Document Arrives
02
AI Extracts & Validates
03
Routed & Done

The Pattern We See Everywhere

It does not matter whether you build buildings, write policies, or manufacture products. The workflow is identical: a request comes in, your estimator opens six systems, pulls data from each one, runs calculations in a spreadsheet, and assembles a proposal document. The bottleneck is not your pricing. It is the time between request and response.

Your best estimator is the bottleneck — every quote waits in their queue because junior staff cannot match their accuracy
Pricing inconsistency across your team costs you margin on some deals and competitiveness on others
Historical quote data and win/loss patterns sit buried in spreadsheets instead of informing the next estimate
Quote-to-close cycle takes so long that prospects go with whoever responds first, not whoever responds best

How This Looks In Your Industry

Same capability. Custom-built for your business.

Estimators receive bid packages with hundreds of pages of drawings, specifications, and addenda. Building a bid means performing material takeoffs, pulling subcontractor pricing, calculating labor, and factoring in equipment and overhead. Your estimator spends 2-3 days on data assembly before they can start on actual pricing strategy.
We build AI that reads project specs and drawings, performs quantity takeoffs, pulls current material pricing from your suppliers, applies your labor rates and productivity factors, and assembles a detailed estimate. Regional cost adjustments, subcontractor historical pricing, and your markup structure are all built in. Your estimator reviews and adjusts the output — focusing on scope interpretation, risk contingency, and competitive positioning instead of data entry.
Estimates assembled in hours, not days · Zero missed addenda
See all Construction workflows →
Quoting a commercial policy means entering the same client data into 4-8 carrier portals, waiting for each to return a rate, then comparing coverage terms and pricing across carriers. Your producers spend more time on portal data entry than on selling. Renewal quotes are worse — the same manual process every 12 months for every client.
We build AI that takes client intake data and submits to multiple carrier quoting systems simultaneously. It normalizes the responses, compares coverage and pricing across carriers, identifies gaps, and generates a comparison presentation your producer can walk through with the client. Renewal pricing pulls prior-year data automatically and flags material changes in coverage or cost.
Multi-carrier quotes in under 1 hour · 100% coverage gap detection
See all Insurance workflows →
Custom product quotes require pulling specs from engineering, calculating material costs from purchasing, applying customer-specific pricing tiers, factoring in volume discounts, and estimating lead times based on current production capacity. Sales waits on three departments before they can respond to the prospect.
We build AI that connects to your ERP, product configurator, and pricing database. It takes the customer's requirements, identifies the right configuration, calculates costs from current material and manufacturing data, applies the customer's pricing tier and applicable discounts, estimates lead time from production schedules, and generates a formatted quote document. Standard configurations quote in minutes. Custom configurations that need engineering route automatically with all context attached.
Standard quotes generated same-day · 95% fewer pricing errors
See all Product Company workflows →

How We Build This

01
AI Assessment
We diagnose your top 3 automation opportunities on a 30-minute call. You tell us where your team spends time. We tell you where AI can help.
Free
02
AI Roadmap
We go deep into your workflows, interview your team, analyze your data systems, and deliver a custom report with ROI projections. If we can't identify at least $25K in annual savings, the roadmap is free.
$1,999 flat
03
Build
We connect data sources, encode pricing logic, build calculation engines, configure output formats, and deploy with review workflows. 2-week sprints with demos at every checkpoint.
$25K–$75K
04
Run
Ongoing pricing updates, rule adjustments, new product configurations, accuracy monitoring, and win-rate analysis. Your quoting gets better over time. 30-day cancel.
$2K–$5K/mo

Results From Similar Implementations

70%
Faster Quoting
40%
Higher Win Rate
45 days
To Production
$350K+
Avg Annual Revenue Impact

Often Built Together

Clients who implement Quoting Automation often pair it with these capabilities.

Document Processing
Extract spec data from RFPs and bid documents to feed quoting. The AI reads incoming requirements so your estimator does not have to.
Risk Analysis
Risk scoring informs pricing accuracy and margin protection. Flag high-risk configurations before the quote goes out, not after the project starts.
Intake Automation
Quote requests flow through automated intake into your quoting pipeline. Structured data from day one, not an email thread your team has to parse.

Stop Losing Deals to Slow Quotes

Get a free 30-minute AI Assessment. We'll identify your top 3 automation opportunities with realistic ROI estimates.

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Common Questions

How accurate are AI-generated quotes compared to manual estimates?
In most implementations, more accurate than manual. Your senior estimator makes good judgment calls but inconsistent math — especially at 4 PM on a Friday with three quotes due. The AI does not get tired, does not round numbers, and does not forget the surcharge on orders under 500 units. It handles data assembly and calculation. Your estimator reviews the output and applies judgment where it matters — scope interpretation, risk factors, relationship pricing. The combination outperforms either alone.
Does this integrate with our existing estimating tools and ERP?
Yes. We build a quoting layer that connects to your existing systems — ERP, product configurator, material pricing databases, supplier catalogs, carrier portals. The AI reads from your data sources and writes back to your systems via API, database connection, or file transfer. Your team keeps using the same tools they use today. We add speed to the front end, not a new back end.
Can it handle complex configurations with hundreds of variables?
That is exactly where automation pays for itself fastest. During assessment, we map every pricing rule, discount tier, exception case, and override scenario your team uses. Volume discounts, seasonal adjustments, material substitutions, customer-specific pricing — all encoded into the quoting logic. When a combination falls outside known rules, the system flags it for human review instead of guessing. The more complex your quoting, the more time the AI saves.
Does the system learn from our historical quotes and win/loss data?
Yes. We ingest your historical quote data during implementation — pricing, configurations, win/loss outcomes, margin results. The system identifies pricing patterns that win, flags estimates outside historical ranges, and recommends adjustments based on competitive positioning. As new quotes close, the system refines its recommendations. This is not a static calculator — it gets better as your data grows.
How long does implementation take to go live?
Most quoting automation implementations go live within 45 days. The first two weeks are assessment — mapping your pricing rules, data sources, and exception cases. Weeks 3-5 are build and configuration. Week 6 is parallel testing where the AI quotes alongside your team. Your involvement is heaviest in week one when we map your quoting process and connect your data sources.